System and method for searching position of a geographical data point in three-dimensional space

ABSTRACT

A system and method for mapping location of one or more target data points to a D-dimensional environment are provided. The method includes mapping a sorted array of D-dimensional polygons to a D-dimensional cartesian coordinate system, determining a floor value and a ceiling value of each coordinate point of each target data point, calculating one or more shifted floor value and one or more shifted ceiling value of each coordinate point, combining the shifted floor value and the shifted ceiling value for determining one or more potential centroids, authenticating the potential centroids for determining one or more real centroids by determining the presence of the potential centroid in the array, calculating the distance of the target data point from the real centroids, and determining the polygon having the real centroid with the least distance from the target data point.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Patent ApplicationNo. PCT/IB2021/055974, filed Jul. 2, 2021, which claims priority fromIndian Patent Application No. 202011027506, filed Jun. 29, 2020, andthese applications are incorporated herein by reference for all purposesas if fully set forth herein.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to systems andmethods for determining the location of a geographical data point in athree-dimensional space. Particularly, the present invention describes atime optimised technique for mapping geographical data points in athree-dimensional space scanned by three-dimensional sensors.

BACKGROUND OF THE INVENTION

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of it being mentioned in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also correspond toimplementations of the claimed technology.

Three-dimensional scanning is a technology widely used for analysingreal-world objects, buildings, landscapes and providing situationalawareness and vision to machines for automation. 3D scanning enablescollecting data regarding the depth, position, shape and appearance thatcould be either further analysed or accurate digital 3D models could beconstructed.

Three-dimensional (3D) scanning may be enabled by means of technologiessuch as Laser Triangulation, Structured Light, Time of Flight, and thelike. Devices such as Light Detection And Ranging (LIDAR), LaserDetection And Ranging (LADAR), Light-emitting diode detection andranging (LeDDAR), Radio Detection and Ranging (RaDAR) and Depth SensingCameras like Microsoft Kinect or Intel RealSense scanners are commonlyused for 3D scanning. LIDAR, LADAR and LEDDAR in general is used formeasuring distances (ranging) by illuminating the target with laserlight and measuring the reflection with a sensor. Similarly RaDAR ingeneral is used for measuring distances (ranging) by illuminating thetarget with Radio Waves in C, K or mmWave Bands. Time of flight oflaser/light or radio wave 30 returns and the return wavelengths and theintensities of the returns can then be used to make digital 3-Drepresentations of the target. LIDAR/RaDAR or Depth Measuring Camerasprovide an output of 3D point clouds producing highly accurate x, y, zmeasurements of reflections and their intensities. 3D point clouds canbe used for a number of applications, such as rendering appealing visualeffect based on the physical properties of 3D structures and cleaning ofraw input 3D point clouds e.g., by removing moving objects (car, bike,person). Other 3D object detection, classification and recognitionindustry domains include agriculture, astronomy, atmosphere, AutonomousVehicles, Biology and conservation, Forestry, Geology and soil science,Law enforcement, Mining, Image Recognition, Surveying, robotics,intelligent vehicle systems, augmented reality, transportation maps andgeological surveys where high resolution digital elevation maps help indetecting subtle topographic features. Some of the major application of3D point cloud and sensing are to create a 3D representation of aterrain's surface, 3D imaging for healthcare, smart devices, topographicanalysis and prediction of soil properties in agricultural landscapes,categories crops based on their characteristics and find the best placesto plant them, mapping the surfaces of celestial bodies, guidance systemfor autonomous vehicles, and the like.

It is essential to map the 3D data points received from a scanningdevice such as a LIDAR to the three-dimensional space before the 3Dpoint cloud could be of any uses. A typical LIDAR could have a data rateof 50million points from an average 16 beam LiDAR and hence time ofprocessing is vital. Similarly processing power or computing power isvital as usually the processing takes place on an embedded device andtherefore the power or processing requirements cannot be stretchedbeyond a limit.

Mapping such large number of 3D data points to the three-dimensionalspace can consume a lot of time and computing power. Moreover, increaseif the range of the three-dimensional space and the granularity of suchspace to be mapped increases the time complexity as well as the requiredcomputing power. Therefore, there exists a need in the art for a timeand computing power optimized system and methods for mapping ordetecting a geographical data point to the corresponding geographicalspace.

Object of the Invention

An object of the present invention is to provide a system and a methodfor mapping, searching, or locating a 3D data point in athree-dimensional space.

Another object of the present invention is to provide a system and amethod that enables a time and computing power optimized solution formapping, searching, or locating a 3D data point in a three-dimensionalspace.

Another object of the present invention is to provide a system and amethod that enables mapping, searching, or locating a 3D data point in athree-dimensional space without increasing the time complexity with theincrease in the range of the scanned three-dimensional space.

Yet another object of the present invention is to provide a system and amethod that enables mapping, searching, or locating a 3D data point in athree-dimensional space without increasing the time complexity with theincrease in the granularity of the scan.

SUMMARY OF THE INVENTION

Now there has been invented an improved method and technical equipmentimplementing the method, by which the above problems are at leastalleviated. Various aspects of the invention include a method, systemand a computer program, which are characterized by what is stated in theindependent claims. Various embodiments of the invention are disclosedin the dependent claims.

In accordance with an embodiment of the present invention, a method formapping location of one or more target data points to a D-dimensionalenvironment is described. The method comprising the steps of obtaining aD-dimensional point-cloud data of the D-dimensional environment from oneor more sensing units, the D-dimensional point-cloud data containinglocation information of each data point of the D-dimensionalenvironment. In an aspect, the one or more sensing units are selectedfrom a group comprising of a LIDAR system, a Ladar, a Leddar, a Radar,and a depth sensing camera.

Thereafter a sorted array of a plurality of D-dimensional polygons isobtained, each D-dimensional polygon having a centroid, wherein thesorted array of the plurality of D-dimensional polygons represents theD-dimensional environment.

Then, mapping the sorted array of the plurality of D-dimensionalpolygons to a cartesian coordinate system. Determining a floor value anda ceiling value of each coordinate point of each target data point.Calculating one or more shifted floor value and one or more shiftedceiling value of each coordinate point of each target data point. In anaspect, the one or more shifted floor value and one or more shiftedceiling value is calculated by shifting the floor value and ceilingvalue of each coordinate point by a corresponding shift size along xaxis and a corresponding shift size along y axis and a correspondingshift along z axis. In another aspect, the one or more shifted ceilingvalue is calculated by shifting the ceiling value of each coordinatepoint by a ceiling shift size.

Combining the one or more shifted floor value and the one or moreshifted ceiling value for determining one or more potential centroids.Authenticating the one or more potential centroids for determining oneor more real centroids by determining the presence of the one or morepotential centroid in the array of centroids. Calculating the distanceof the target data point from the one or more real centroids; anddetermining the polygon having the real centroid with the least distancefrom the target data point.

In another embodiment, a system for mapping location of one or moretarget data points in a three-dimensional environment is described. Thesystem comprises one or more sensing units, at least one memory unit,and a processing unit. The one or more sensing units are configured forobtaining a D-dimensional point-cloud data of the D-dimensionalenvironment from one or more sensing units, the D-dimensionalpoint-cloud data containing location information of each data point ofthe D-dimensional environment. In an aspect, the one or more sensingunits are selected from a group comprising of a LIDAR system, a Ladar, aLeddar, a Radar, and a depth sensing camera.

The memory unit may be configured for storing a plurality ofinstructions, and a D-dimensional point-cloud representation of theD-dimensional environment, the D-dimensional point-cloud representationcontaining location information of each data point of the D-dimensionalenvironment. The processing unit coupled with a memory may be configuredfor obtaining a sorted array of a plurality of D-dimensional polygons,each D-dimensional polygon having a centroid, wherein the sorted arrayof the plurality of D-dimensional polygons represents the D-dimensionalenvironment.

The processing unit may be communicatively coupled with the memory. Theprocessing unit may execute the plurality of instructions for mappingthe sorted array of the plurality of D-dimensional polygons to acartesian coordinate system; converting the D-dimensional point-clouddata into a sorted array of a plurality of D-dimensional polygons,wherein each D-dimensional polygon has a centroid; calculating a floorvalue and a ceiling value of each coordinate point of each target datapoint; calculating one or more shifted floor value and one or moreshifted ceiling value of each coordinate point of each target datapoint; combining the one or more shifted floor value and the one or moreshifted ceiling value for determining one or more potential centroids;authenticating the one or more potential centroids for determining oneor more real centroids by determining the presence of the one or morepotential centroid in the array of centroids; calculating the distanceof the target data point from the one or more real centroids; anddetermining the polygon having the real centroid with the least distancefrom the target data point.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular to thedescription of the invention, briefly summarized above, may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments of this invention and are thereforenot to be considered limiting of its scope, the invention may admit toother equally effective embodiments. These and other features, benefitsand advantages of the present invention will become apparent byreference to the following text figure, with like reference numbersreferring to like structures across the views, wherein:

FIG. 1 illustrates a sorted array of polygons with a centroid, accordingto an embodiment of the present invention.

FIG. 2 illustrates a sorted array of polygons mapped to a cartesiancoordinate system, according to an embodiment of the present invention.

FIG. 3 illustrates a method for mapping one or more target data pointsto a d-dimensional environment, according to an embodiment of thepresent invention.

FIG. 4 illustrates a system for mapping one or more target data pointsto a d10 dimensional environment, according to an embodiment of thepresent invention.

DETAILED DESCRIPTION OF DRAWINGS

While the present invention is described herein by way of example usingembodiments and illustrative drawings, those skilled in the art willrecognize that the invention is not limited to the embodiments ofdrawing or drawings described and are not intended to represent thescale of the various components. Further, some components that may forma part of the invention may not be illustrated in certain figures, forease of illustration, and such omissions do not limit the embodimentsoutlined in any way. It should be understood that the drawings anddetailed description thereto are not intended to limit the invention tothe particular form disclosed, but on the contrary, the invention is tocover all modifications, equivalents, and alternatives falling withinthe scope of the present invention as defined by the appended claims. Asused throughout this description, the word “may” is used in a permissivesense (i.e. meaning having the potential to), rather than the mandatorysense, (i.e. meaning must). Further, the words “a” or “an” mean “atleast one” and 25 the word “plurality” means “one or more” unlessotherwise mentioned. Furthermore, the terminology and phraseology usedherein is solely used for descriptive purposes and should not beconstrued as limiting in scope. Language such as “including,”“comprising,” “having,” “containing,” or “involving,” and variationsthereof, is intended to be broad and encompass the subject matter listedthereafter, equivalents, and 30 additional subject matter not recited,and is not intended to exclude other additives, components, integers orsteps. Likewise, the term “comprising” is considered synonymous with theterms “including” or “containing” for applicable legal purposes. Anydiscussion of documents, acts, materials, devices, articles and the likeis included in the specification solely for the purpose of providing acontext for the present invention. It is not suggested or representedthat any or all of these matters form part of the prior art base or werecommon general knowledge in the field relevant to the present invention.

In this disclosure, whenever a composition or an element or a group ofelements is preceded with the transitional phrase “comprising”, it isunderstood that we also contemplate the same composition, element orgroup of elements with transitional phrases “consisting of”,“consisting”, “selected from the group of consisting of, “including”, or“is” preceding the recitation of the composition, element or group ofelements and vice versa.

The present invention is described hereinafter by various embodimentswith reference to the accompanying drawings, wherein reference numeralsused in the accompanying drawing correspond to the like elementsthroughout the description. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiment set forth herein. Rather, the embodiment is provided so thatthis disclosure will be thorough and complete and will fully convey thescope of the invention to those skilled in the art. In the followingdetailed description, numeric values and ranges are provided for variousaspects of the implementations described. These values and ranges are tobe treated as examples only and are not intended to limit the scope ofthe claims. In addition, a number of materials are identified assuitable for various facets of the implementations. These materials areto be treated as exemplary and are not intended to limit the scope ofthe invention.

In accordance with an embodiment of the present invention, a scanningdevice scans a d-dimensional space and provides a point cloud data. Suchpoint cloud data comprises of information related to a plurality ofpoints present in the d-dimensional space. In an aspect, the point clouddata may include location information such as (x, y, z) coordinates ofeach point present in the d-dimensional space. Apart from the (x, y, z)coordinates the point cloud may also provide information regardingcolor, intensity, and luminosity of each point present in thed-dimensional space. The scanning device along with an associatedprocessing unit may convert the point cloud into an interlocked grid ofa plurality of regular polygons. These polygons may have d-dimensionsbased on the information captured by the scanning device. Each suchpolygon may comprise of a plurality of data points present in thed-dimensional space. Each such polygon has a centroid.

In FIG. 1 a sorted array of polygons with a centroid is illustratedaccording to an embodiment of the present invention. The figureillustrates a 2-dimensional array of polygons, however, a person skilledin the art would appreciate that dimensions of polygons depend on thedimensions of the scanned environment. For example, if a 2D drawing isscanned then the polygons would have 2-dimensions. In another example,if a three-dimensional environment such as a road with vehicles isscanned then the polygons would have 3-dimensions. For ease ofunderstanding the interlocked grid of polygons represented in FIG. 1 are2-dimensional. Polygon (102) is a regular hexagon with a centroid (104).Polygon (102) may comprise a plurality of data points depending on thecluster of points present in the point cloud provided by the scanningdevice. The present invention provides methods, systems, devices, andcomputer program for mapping these plurality of data points to suchpolygons. Upon mapping the data points to polygons further processing isenabled for further analytics such as object recognition.

The interlocked grid of D-dimensional polygons are then mapped to acartesian coordinate system having D-dimensions. FIG. 2 illustrates asorted array of polygons mapped to a cartesian coordinate system,according to an embodiment of the present invention. For ease ofunderstanding a 2-dimensional coordinate system is illustrated andmapped to 2-dimensional polygons. A target data point A (202) isillustrated in the figure having coordinates (x,y). For example, thetarget data point A (202) has coordinates (4.25, 1.35), where abscissais equal to 4.25 and ordinate is equal to 1.35. The polygon (204) havingthe centroid (206) may contain the target data point A (202) which willbe determined in accordance to the method described in FIG. 3 . Itshould be noted that the sorted array of polygons or sorted array ofcentroids is interchangeably used throughout the present disclosure.

Further, upon mapping the array of regular polygons may result into thebottom-left vertex of a first polygon to be mapped with (X=0, Y=0). Thismay result into an offset between the coordinate system and the array ofcentroids of the regular polygons. A shift size is determined along thex axis and the y axis based on the distance of the centroid of the firstpolygon from (X=0, Y=0).

FIG. 3 illustrates a method for mapping one or more target data pointsto a d-dimensional environment, according to an embodiment of thepresent invention. At step 302, D-dimensional point-cloud data of theD-dimensional environment is obtained from one or more sensing units orscanning devices. The D-dimensional point-cloud data contains locationinformation of each data point of the D-dimensional environment. At step304, the D-dimensional point-cloud data may be converted into a sortedarray of a plurality of D-dimensional polygons, wherein eachD-dimensional polygon has a centroid. Alternatively, the processorcoupled with the memory may create a sorted array of a plurality ofD-dimensional polygons.

Then at step 306, the plurality of centroids of the D-dimensionalpolygons is mapped to a D-dimensional cartesian coordinate system. Forexample, if the polygons are a three-dimensional polygon, then athree-dimensional cartesian coordinate system is used for mapping.

At step 308, a floor value and a ceiling value of each coordinate pointof each target data point is calculated. Here, floor and ceiling valuesof the abscissa and the ordinate value of the target data point aredetermined. For example, the target data point A (202) has coordinates(4.25, 1.35), hence the floor value of abscissa of point A would be 4and the ceiling value of abscissa of point A would be 5. Similarly, thefloor value of ordinate of point A would be 1 and the ceiling value ofordinate of point A would be 2.

At step 310, shifted floor value and shifted ceiling value of eachcoordinate point of the target data point is calculated. In an aspect,the shift size is by calculating the distance of the centroid of thefirst polygon from (X=0, Y=0) along the x axis and the y axis. Forexample, for the target data point A (202) the shift size along the xaxis would be 0.15 and the shift size along the y axis would be 0.25.Therefore, the shifted floor and ceiling values for target data point A(202) would be ((X1=4.15, X2=5.15), (Y1=1.25, Y2=2.25)).

Now at step 312, the shifted floor and ceiling values of the abscissaand ordinate of the target data point A (202) are combined to determineone or more potential centroids. For example, if the shifted floor andceiling values for target data point A (202) are ((X1=4.15, X2=5.15),(Y1=1.25, Y2=2.25)) then the potential centroids would be (X1, Y1), (X2,Y2), (X1, Y2), (X2, Y1) resulting in the potential centroids as(4.15,1.25), (5.15, 2.25), (4.15,2.25), (5.15, 1.25).

At step 314, the potential centroids are authenticated for determiningone or more real centroids by determining the presence of the one ormore potential centroid in the array of centroids. A real centroid wouldbe present as a centroid of a single polygon in the sorted array ofpolygons. For the present example, only (4.15,1.25) and (4.15,2.25) arepresent as a centroid in the sorted array of polygons. Hence, these 2points are stored as real centroids.

Now for determining the polygon is which the target data point A (202)is present, at step 316 distance is calculated between the target datapoint from the real centroids. For the present example, distance betweenthe target data point A (202) having coordinates (4.25, 1.35) iscalculated from determined real centroids (4.15,1.25) and (4.15,2.25).At step 318, it is clearly seen that the target data point A (202) isclosest to centroid (4.15,1.25) in comparison to (4.15,2.25), hence thepolygon having (4.15,1.25) as centroid is the polygon containing thetarget data point A.

The steps as disclosed above are either iteratively or in parallel canbe executed for mapping all the data points to the polygons of thed-dimensional space.

FIG. 4 illustrates a system (400) for mapping one or more target datapoints to a d-dimensional environment, according to an embodiment of thepresent invention.

It should be note that the system could be implemented as an embeddedsystem or a distributed system. For example, the memory unit (404) andone or more sensing unit (402) could be present in a separate device andat different location from the processing unit (406) and display unit(408) which could be implemented in a server-side computing device. Inanother example, the system (400) may be an autonomous vehicle or aself-driving car.

In principle the one or more sensing units (402) scan the d-dimensionalspace and the data collected is stored in memory unit (404). The storeddata is then retrieved from the memory unit (404) by the processing unit(406) for executing the method steps as described in FIG. 3 . Theprocessing unit (406) and the scanning module (402) may further beconnected to the display unit (408) for displaying the scanned imagesand for further analysis. In an aspect, the processing unit (406) may bea parallel processing unit.

The one or more sensing units (402) are configured for obtaining aD-dimensional point-cloud data of the D-dimensional environment from oneor more sensing units, the D-dimensional point-cloud data containinglocation information of each data point of the D-dimensionalenvironment. In an aspect, the one or more sensing units are selectedfrom a group comprising of a LIDAR system, a Ladar, a Leddar, a Radar,and a depth sensing camera.

The memory unit (404) may be configured for storing a plurality ofinstructions, and a D-dimensional point-cloud representation of theD-dimensional environment, the D-dimensional point-cloud representationcontaining location information of each data point of the D-dimensionalenvironment.

The processing unit (406) may be communicatively coupled with thememory. The processing unit may execute the plurality of instructionsfor mapping the sorted array of the plurality of D-dimensional polygonsto a cartesian coordinate system; converting the D-dimensionalpoint-cloud data into a sorted array of a plurality of D-dimensionalpolygons, wherein each D-dimensional polygon has a centroid; calculatinga floor value and a ceiling value of each coordinate point of eachtarget data point; calculating one or more shifted floor value and oneor more shifted ceiling value of each coordinate point of each targetdata point; combining the one or more shifted floor value and the one ormore shifted ceiling value for determining one or more potentialcentroids; authenticating the one or more potential centroids fordetermining one or more real centroids by determining the presence ofthe one or more potential centroid in the array of centroids;calculating the distance of the target data point from the one or morereal centroids; and determining the polygon having the real centroidwith the least distance from the target data point.

In another embodiment, a computer program product for causing a computerto map location of one or more target data points to a D-dimensionalenvironment is disclosed. The computer program product may be stored ina computer memory, the computer memory being coupled to one or morecentral processing units, graphics processing units or acceleratedprocessing units.

The computer program product may be embodied for example as a storagedevice carrying instructions or a signal carrying instructions,comprising instructions for programming a programmable processingapparatus to become operable to perform a method as set out above or tobecome configured as an apparatus as set out above. In an aspect, theapparatus may be an apparatus related to agriculture, astronomy,atmosphere, Autonomous Vehicles, Biology and conservation, Forestry,Geology and soil science, Law enforcement, Mining, 3D imaging forhealthcare, Image Recognition, Surveying, robotics, intelligent vehiclesystems, augmented reality, transportation maps and geological surveyswhere high resolution digital elevation maps help in detecting subtletopographic features.

The computer program product when executed by the processing units causeobtaining a D-dimensional point-cloud data of the D-dimensionalenvironment and one or more target data points from one or more sensingunits, the D-dimensional point-cloud data containing locationinformation of each data point of the D-dimensional environment.Obtaining a sorted array of a plurality of D-dimensional polygons, eachD-dimensional polygon having a centroid, wherein the sorted array of theplurality of D-dimensional polygons represents the D-dimensionalenvironment. Mapping the sorted array of the plurality of D-dimensionalpolygons to a D-dimensional cartesian coordinate system. Determining afloor value and a ceiling value of each coordinate point of each targetdata point. Calculating one or more shifted floor value and one or moreshifted ceiling value of each coordinate point of each target datapoint. Combining the one or more shifted floor value and the one or moreshifted ceiling value for determining one or more potential centroids.Authenticating the one or more potential centroids for determining oneor more real centroids by determining the presence of the one or morepotential centroid in the array of centroids. Calculating the distanceof the target data point from the one or more real centroids; anddetermining the polygon having the real centroid with the least distancefrom the target data point.

In another embodiment, a non-transitory computer-readable media havingstored thereon instructions, which when executed by one or moreprocessors, cause one or more processors to map location of one or moretarget data points to a D-dimensional environment. The non-transitorycomputer-readable media enable obtaining a D-dimensional point-clouddata of the D-dimensional environment and one or more target data pointsfrom one or more sensing units, the D-dimensional point-cloud datacontaining location information of each data point of the D-dimensionalenvironment. Obtaining a sorted array of a plurality of D-dimensionalpolygons, each D-dimensional polygon having a centroid, wherein thesorted array of the plurality of D-dimensional polygons represents theD-dimensional environment. Mapping the sorted array of the plurality ofD-dimensional polygons to a D-dimensional cartesian coordinate system.Determining a floor value and a ceiling value of each coordinate pointof each target data point. Calculating one or more shifted floor valueand one or more shifted ceiling value of each coordinate point of eachtarget data point. Combining the one or more shifted floor value and theone or more shifted ceiling value for determining one or more potentialcentroids.

Authenticating the one or more potential centroids for determining oneor more real centroids by determining the presence of the one or morepotential centroid in the array of centroids. Calculating the distanceof the target data point from the one or more real centroids; anddetermining the polygon having the real centroid with the least distancefrom the target data point.

In another embodiment of the invention a programmable processingapparatus is disclosed, such as a personal computer (PC), containing, ina conventional manner, one or more processors, memories, graphics cardsetc. together with a display device, such as a conventional personalcomputer monitor, and user input devices, such as a keyboard, mouse etc.The programmable processing apparatus is programmed to operate inaccordance with programming instructions input, for example, as datastored on a data storage medium (such as an optical CD ROM,semiconductor ROM, magnetic recording medium, eta), and/or as a signal(for example an electrical or optical signal input to the processingapparatus, for example from a remote database, by transmission over acommunication network (not shown) such as the Internet or bytransmission through the atmosphere), and/or entered by a user via auser input device such as a keyboard. The programmable processingapparatus may enable obtaining a D-dimensional point-cloud data of theD-dimensional environment and one or more target data points from one ormore sensing units, the D-dimensional point-cloud data containinglocation information of each data point of the D-dimensionalenvironment. Obtaining a sorted array of a plurality of D-dimensionalpolygons, each D-dimensional polygon having a centroid, wherein thesorted array of the plurality of D-dimensional polygons represents theD-dimensional environment. Mapping the sorted array of the plurality ofD-dimensional polygons to a D-dimensional cartesian coordinate system.Determining a floor value and a ceiling value of each coordinate pointof each target data point. Calculating one or more shifted floor valueand one or more shifted ceiling value of each coordinate point of eachtarget data point. Combining the one or more shifted floor value and theone or more shifted ceiling value for determining one or more potentialcentroids. Authenticating the one or more potential centroids fordetermining one or more real centroids by determining the presence ofthe one or more potential centroid in the array of centroids.Calculating the distance of the target data point from the one or morereal centroids; and determining the polygon having the real centroidwith the least distance from the target data point.

The present invention offers a number of advantages. Firstly, thepresent invention provides a simple, cost-effective and easy to usesolution for the problems of prior art. The existing technology thatrelies on brute force method of mapping/searching target data point inthe polygons in three-dimensional space is computationally expensive asall the polygons need to be searched for the target data point. On theother hand, with the present invention the time of processing or thetime complexity as well as the computational requirements remainconstant irrespective of the increase in the granularity or the range ofthe scanner as for a three-dimensional space only 9 valid polygons(3{circumflex over ( )}3) need to be calculated for nearest centroid.This does not change even if the number of polygons increase in the 3Dspace.

In general, the word “module” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,written in a programming language, such as, for example, Java, C, Pythonor assembly. One or more software instructions in the modules may beembedded in firmware, such as an EPROM. It will be appreciated thatmodules may comprised connected logic units, such as gates andflip-flops, and may comprise programmable units, such as programmablegate arrays or processors. The modules described herein may beimplemented as either software and/or hardware modules and may be storedin any type of computer-readable medium or other computer storagedevice.

Further, while one or more operations have been described as beingperformed by or otherwise related to certain modules, devices orentities, the operations may be performed by or otherwise related to anymodule, device or entity. As such, any function or operation that hasbeen described as being performed by a module could alternatively beperformed by a different server, by the cloud computing platform, or acombination thereof. It should be understood that the techniques of thepresent disclosure might be implemented using a variety of technologies.For example, the methods described herein may be implemented by a seriesof computer executable instructions residing on a suitable computerreadable medium. Suitable computer readable media may include volatile(e.g., RAM) and/or non-volatile (e.g., ROM, disk) memory, carrier wavesand transmission media. Exemplary carrier waves may take the form ofelectrical, electromagnetic or optical signals conveying digital datasteams along a local network or a publicly accessible network such asthe Internet.

It should also be understood that, unless specifically stated otherwiseas apparent from the following discussion, it is appreciated thatthroughout the description, discussions utilizing terms such as“controlling” or “obtaining” or “computing” or “storing” or “receiving”or “determining” or the like, refer to the action and processes of acomputer system, or similar electronic computing device, that processesand transforms data represented as physical (electronic) quantitieswithin the computer system's registers and memories into other datasimilarly represented as physical quantities within the computer systemmemories or registers or other such information storage, transmission ordisplay devices.

Various modifications to these embodiments are apparent to those skilledin the art from the description and the accompanying drawings. Theprinciples associated with the various embodiments described herein maybe applied to other embodiments. Therefore, the description is notintended to be limited to the embodiments shown along with theaccompanying drawings but is to be providing broadest scope ofconsistent with the principles and the novel and inventive featuresdisclosed or suggested herein. Accordingly, the invention is anticipatedto hold on to all other such alternatives, modifications, and variationsthat fall within the scope of the present invention and the appendedclaims.

We claim:
 1. A processor implemented method for mapping location of oneor more target data points to a D-dimensional environment, the methodcomprising the step of: obtaining a D-dimensional point-cloud data ofthe D-dimensional environment and one or more target data points fromone or more sensing units, the D-dimensional point-cloud data containinglocation information of each data point of the D-dimensionalenvironment; obtaining a sorted array of a plurality of D-dimensionalpolygons, each D-dimensional polygon having a centroid, wherein thesorted array of the plurality of D-dimensional polygons represents theD-dimensional environment; mapping the sorted array of the plurality ofD-dimensional polygons to a D-dimensional cartesian coordinate system;determining a floor value and a ceiling value of each coordinate pointof each target data point; calculating one or more shifted floor valueand one or more shifted ceiling value of each coordinate point of eachtarget data point; combining the one or more shifted floor value and theone or more shifted ceiling value for determining one or more potentialcentroids; authenticating the one or more potential centroids fordetermining one or more real centroids by determining the presence ofthe one or more potential centroid in the array of centroids;calculating the distance of the target data point from the one or morereal centroids; and determining the polygon having the real centroidwith the least distance from the target data point.
 2. The method asclaimed in claim 1, where the one or more sensing units are selectedfrom a group comprising of a LIDAR system, a Ladar, a Leddar, a Radar,and a depth sensing camera.
 3. The method as claimed in claim 1, whereinthe one or more shifted floor value and one or more shifted ceilingvalue is calculated by shifting the floor value and ceiling value ofeach coordinate point by a corresponding shift size along x axis, acorresponding shift size along y axis, and a corresponding shift sizealong z axis.
 4. The method as claimed in claim 1, wherein the one ormore shifted floor value is calculated by shifting the floor value ofeach coordinate point by a floor shift size.
 5. The method as claimed inclaim 1, wherein the one or more shifted ceiling value is calculated byshifting the ceiling value of each coordinate point by a ceiling shiftsize.
 6. A system for mapping location of one or more target data pointsin a three-dimensional environment, the system comprising: one or moresensing units for obtaining a D-dimensional point-cloud data of theD-dimensional environment from one or more sensing units, theD-dimensional point-cloud data containing location information of eachdata point of the D-dimensional environment; obtaining a sorted array ofa plurality of D-dimensional polygons, each D-dimensional polygon havinga centroid, wherein the sorted array of the plurality of D-dimensionalpolygons represents the D-dimensional environment; a memory unit forstoring a plurality of instructions, and a D-dimensional point-cloudrepresentation of the D-dimensional environment, the D-dimensionalpoint-cloud representation containing location information of each datapoint of the D-dimensional environment; a processing unitcommunicatively coupled with the memory unit, wherein the processingunit executes the plurality of instructions for: mapping the sortedarray of the plurality of D-dimensional polygons to a D-dimensionalcartesian coordinate system; determining a floor value and a ceilingvalue of each coordinate point of each target data point; calculatingone or more shifted floor value and one or more shifted ceiling value ofeach coordinate point of each target data point; combining the one ormore shifted floor value and the one or more shifted ceiling value fordetermining one or more potential centroids; authenticating the one ormore potential centroids for determining one or more real centroids bydetermining the presence of the one or more potential centroid in thearray of centroids; calculating the distance of the target data pointfrom the one or more real centroids; and determining the polygon havingthe real centroid with the least distance from the target data point. 7.The system as claimed in claim 6, wherein the one or more sensing unitsare selected from a group comprising of a LIDAR system, a Ladar, aLeddar, a Radar, and a depth sensing camera.
 8. A computer programproduct comprising a computer useable medium having computer programlogic for enabling at least one processor to map location of one or moretarget data points in a three-dimensional environment, said 20 computerlogic comprising: obtaining a D-dimensional point-cloud data of theD-dimensional environment and one or more target data points from one ormore sensing units, the D-dimensional point-cloud data containinglocation information of each data point of the D-dimensionalenvironment; obtaining a sorted array of a plurality of D-dimensionalpolygons, each D-dimensional polygon having a centroid, wherein thesorted array of the plurality of D-dimensional polygons represents theD-dimensional environment; mapping the sorted array of the plurality ofD-dimensional polygons to a D-dimensional cartesian coordinate system;determining a floor value and a ceiling value of each coordinate pointof each target data point; calculating one or more shifted floor valueand one or more shifted ceiling value of each coordinate point of eachtarget data point; combining the one or more shifted floor value and theone or more shifted ceiling value for determining one or more potentialcentroids; authenticating the one or more potential centroids fordetermining one or more real centroids by determining the presence ofthe one or more potential centroid in the array of centroids;calculating the distance of the target data point from the one or morereal centroids; and determining the polygon having the real centroidwith the least distance from the target data point.
 9. An apparatuscomprising one or more sensing unit, a memory unit, and a processingunit, wherein the sensing unit, the memory unit, and the processing unitare communicatively coupled for performing the steps of: obtaining aD-dimensional point-cloud data of the D-dimensional environment and oneor more target data points from one or more sensing units, theD-dimensional point-cloud data containing location information of eachdata point of the D-dimensional environment; obtaining a sorted array ofa plurality of D-dimensional polygons, each D-dimensional polygon havinga centroid, wherein the sorted array of the plurality of D-dimensionalpolygons represents the D-dimensional environment; mapping the sortedarray of the plurality of D-dimensional polygons to a D-dimensionalcartesian coordinate system; determining a floor value and a ceilingvalue of each coordinate point of each target data point; calculatingone or more shifted floor value and one or more shifted ceiling value ofeach coordinate point of each target data point; combining the one ormore shifted floor value and the one or more shifted ceiling value fordetermining one or more potential centroids; authenticating the one ormore potential centroids for determining one or more real centroids bydetermining the presence of the one or more potential centroid in thearray of centroids; calculating the distance of the target data pointfrom the one or more real centroids; and determining the polygon havingthe real centroid with the least distance from the target data point.